AFSA-SLnO variants for enhanced global optimization
Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to a...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39634/ http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf |
| _version_ | 1848825820869558272 |
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| author | Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman |
| author_facet | Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman |
| author_sort | Norazian, Subari |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to achieve a satisfactory global optimum. This paper presents incorporation of circle updating position from Sea Lion Optimization (SLnO) into AFSA to enhance the robustness and optimum value. Fifteen benchmarks function have been used to evaluate the performance of the proposed variants in comparison to the standard AFSA and SLnO. The proposed variants show better result compared to the standard AFSA and SLnO. |
| first_indexed | 2025-11-15T03:35:00Z |
| format | Conference or Workshop Item |
| id | ump-39634 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:35:00Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-396342023-12-13T04:14:38Z http://umpir.ump.edu.my/id/eprint/39634/ AFSA-SLnO variants for enhanced global optimization Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Artificial fish swarm algorithm (AFSA) is a strategy which imitates the natural behavior of fish swarm in the real environment. Many improvements and modifications have been proposed on AFSA to improve its optimization performance. To date, nevertheless, the existing algorithms are still unable to achieve a satisfactory global optimum. This paper presents incorporation of circle updating position from Sea Lion Optimization (SLnO) into AFSA to enhance the robustness and optimum value. Fifteen benchmarks function have been used to evaluate the performance of the proposed variants in comparison to the standard AFSA and SLnO. The proposed variants show better result compared to the standard AFSA and SLnO. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf pdf en http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf Norazian, Subari and Junita, Mohamad-Saleh and Noorazliza, Sulaiman (2022) AFSA-SLnO variants for enhanced global optimization. In: Lecture Notes in Electrical Engineering; 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021 , 5-6 April 2021 , Virtual, Online. pp. 513-522., 829 LNEE (272139). ISSN 1876-1100 ISBN 978-981168128-8 (Published) https://doi.org/10.1007/978-981-16-8129-5_79 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Norazian, Subari Junita, Mohamad-Saleh Noorazliza, Sulaiman AFSA-SLnO variants for enhanced global optimization |
| title | AFSA-SLnO variants for enhanced global optimization |
| title_full | AFSA-SLnO variants for enhanced global optimization |
| title_fullStr | AFSA-SLnO variants for enhanced global optimization |
| title_full_unstemmed | AFSA-SLnO variants for enhanced global optimization |
| title_short | AFSA-SLnO variants for enhanced global optimization |
| title_sort | afsa-slno variants for enhanced global optimization |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39634/ http://umpir.ump.edu.my/id/eprint/39634/ http://umpir.ump.edu.my/id/eprint/39634/1/AFSA-SLnO%20Variants%20for%20Enhanced%20Global%20Optimization.pdf http://umpir.ump.edu.my/id/eprint/39634/2/AFSA-SLnO%20variants%20for%20enhanced%20global%20optimization_ABS.pdf |